{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3.stock_prediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "sHEdIovSDZ0l",
        "colab_type": "code",
        "outputId": "ea5de3b9-e124-4614-c361-60f0bac0d73d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0-alpha0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 332.1MB 51kB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0)\n",
            "Collecting google-pasta>=0.1.2 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/bb/f1bbc131d6294baa6085a222d29abadd012696b73dcbf8cf1bf56b9f082a/google_pasta-0.1.5-py3-none-any.whl (51kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 19.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.16.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n",
            "Collecting tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
            "\u001b[K    100% |████████████████████████████████| 419kB 11.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n",
            "Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 3.0MB 5.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (40.9.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1)\n",
            "Installing collected packages: google-pasta, tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed google-pasta-0.1.5 tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VBXq-Wb_K1XR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.layers import Bidirectional, Dropout, Activation, Dense, LSTM\n",
        "from tensorflow.python.keras.layers import CuDNNLSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y5o6dHO2LLz-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Data comes from:\n",
        "# https://finance.yahoo.com/quote/BTC-USD/history?period1=1279314000&period2=1556053200&interval=1d&filter=history&frequency=1d\n",
        "\n",
        "csv_path = \"https://raw.githubusercontent.com/curiousily/Deep-Learning-For-Hackers/master/data/3.stock-prediction/BTC-USD.csv\"\n",
        "# csv_path = \"https://raw.githubusercontent.com/curiousily/Deep-Learning-For-Hackers/master/data/3.stock-prediction/AAPL.csv\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BnuBLC5DLjXH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(csv_path, parse_dates=['Date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kqMRRLeTLk_P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = df.sort_values('Date')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mU4B3eNtLpgH",
        "colab_type": "code",
        "outputId": "b61de067-73e6-4add-fc40-2ccd847d1349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-07-16</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-07-17</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.08585</td>\n",
              "      <td>0.05941</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-07-18</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.09307</td>\n",
              "      <td>0.07723</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-07-19</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08181</td>\n",
              "      <td>0.07426</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-07-20</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.06634</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date     Open     High      Low    Close  Adj Close  Volume\n",
              "0 2010-07-16  0.04951  0.04951  0.04951  0.04951    0.04951       0\n",
              "1 2010-07-17  0.04951  0.08585  0.05941  0.08584    0.08584       5\n",
              "2 2010-07-18  0.08584  0.09307  0.07723  0.08080    0.08080      49\n",
              "3 2010-07-19  0.08080  0.08181  0.07426  0.07474    0.07474      20\n",
              "4 2010-07-20  0.07474  0.07921  0.06634  0.07921    0.07921      42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "t-xB1zK0C0mG",
        "colab_type": "code",
        "outputId": "12a8abf5-4c5e-45c5-8a57-8c9f43fcf0a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3201, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "4XyoR5lG3Jxv",
        "colab_type": "code",
        "outputId": "c024bbc8-9101-43cb-fc71-02803e60e315",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "cell_type": "code",
      "source": [
        "ax = df.plot(x='Date', y='Close');\n",
        "ax.set_xlabel(\"Date\")\n",
        "ax.set_ylabel(\"Close Price (USD)\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Close Price (USD)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAHgCAYAAAACOkT5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7twAH-I-qO9Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Normalization"
      ]
    },
    {
      "metadata": {
        "id": "Ux2FtgMAqObq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "close_price = df.Close.values.reshape(-1, 1)\n",
        "\n",
        "scaled_close = scaler.fit_transform(close_price)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UprkaMWFvQOG",
        "colab_type": "code",
        "outputId": "50828dd8-aa18-43af-998b-82e99fd8e8a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "scaled_close.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3201, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "qOg2HX9AuTk-",
        "colab_type": "code",
        "outputId": "a5666c09-544a-4115-b89b-ae5ee76537a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.isnan(scaled_close).any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "evHUrll9ukht",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaled_close = scaled_close[~np.isnan(scaled_close)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gCyOvwnqvTSw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaled_close = scaled_close.reshape(-1, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eSJxuT3-u6K2",
        "colab_type": "code",
        "outputId": "e13df5e4-1efa-4734-8d46-3d700e4ea22b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.isnan(scaled_close).any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "CQgrA2va4Jzx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "metadata": {
        "id": "NS5O4-Vtq-N1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "SEQ_LEN = 100\n",
        "\n",
        "def to_sequences(data, seq_len):\n",
        "    d = []\n",
        "\n",
        "    for index in range(len(data) - seq_len):\n",
        "        d.append(data[index: index + seq_len])\n",
        "\n",
        "    return np.array(d)\n",
        "\n",
        "def preprocess(data_raw, seq_len, train_split):\n",
        "\n",
        "    data = to_sequences(data_raw, seq_len)\n",
        "\n",
        "    num_train = int(train_split * data.shape[0])\n",
        "\n",
        "    X_train = data[:num_train, :-1, :]\n",
        "    y_train = data[:num_train, -1, :]\n",
        "\n",
        "    X_test = data[num_train:, :-1, :]\n",
        "    y_test = data[num_train:, -1, :]\n",
        "\n",
        "    return X_train, y_train, X_test, y_test\n",
        "\n",
        "\n",
        "X_train, y_train, X_test, y_test = preprocess(scaled_close, SEQ_LEN, train_split = 0.95)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XNBTUeaXreni",
        "colab_type": "code",
        "outputId": "8e2462c7-6df4-4378-8f20-c610c1e3580d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2945, 99, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "4IOoiN3pUUgO",
        "colab_type": "code",
        "outputId": "f5a1ae55-fc14-4af6-f9d5-116e0b32c6b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 99, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "Yhhv26c34YTW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Model"
      ]
    },
    {
      "metadata": {
        "id": "a3dw5qTasJoy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "DROPOUT = 0.2\n",
        "WINDOW_SIZE = SEQ_LEN - 1\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM(WINDOW_SIZE, return_sequences=True),\n",
        "                        input_shape=(WINDOW_SIZE, X_train.shape[-1])))\n",
        "model.add(Dropout(rate=DROPOUT))\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM((WINDOW_SIZE * 2), return_sequences=True)))\n",
        "model.add(Dropout(rate=DROPOUT))\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM(WINDOW_SIZE, return_sequences=False)))\n",
        "\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "model.add(Activation('linear'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pjBan-K27L8d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training"
      ]
    },
    {
      "metadata": {
        "id": "zvc-LMgOHkWJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss='mean_squared_error', \n",
        "    optimizer='adam'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m1aU2xDvsvrN",
        "colab_type": "code",
        "outputId": "cf1ac72f-ca97-4b5d-fb2e-2d5614e1d8ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1734
        }
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 64\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, \n",
        "    y_train, \n",
        "    epochs=50, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=False,\n",
        "    validation_split=0.1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2650 samples, validate on 295 samples\n",
            "Epoch 1/50\n",
            "2650/2650 [==============================] - 11s 4ms/sample - loss: 0.0011 - val_loss: 0.0542\n",
            "Epoch 2/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0075 - val_loss: 0.0042\n",
            "Epoch 3/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0110 - val_loss: 0.0217\n",
            "Epoch 4/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0058 - val_loss: 0.0028\n",
            "Epoch 5/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0142 - val_loss: 0.0567\n",
            "Epoch 6/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0061 - val_loss: 0.0023\n",
            "Epoch 7/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0123 - val_loss: 0.0425\n",
            "Epoch 8/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0092 - val_loss: 0.0111\n",
            "Epoch 9/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0257 - val_loss: 0.0466\n",
            "Epoch 10/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0165 - val_loss: 0.0663\n",
            "Epoch 11/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0058 - val_loss: 0.0057\n",
            "Epoch 12/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0199 - val_loss: 0.0517\n",
            "Epoch 13/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0132 - val_loss: 0.0623\n",
            "Epoch 14/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0190 - val_loss: 0.0860\n",
            "Epoch 15/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0148 - val_loss: 0.0793\n",
            "Epoch 16/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0141 - val_loss: 0.0690\n",
            "Epoch 17/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0118 - val_loss: 0.0574\n",
            "Epoch 18/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0054 - val_loss: 0.0259\n",
            "Epoch 19/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0083 - val_loss: 0.0586\n",
            "Epoch 20/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0056 - val_loss: 0.0154\n",
            "Epoch 21/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0079 - val_loss: 0.0267\n",
            "Epoch 22/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0055 - val_loss: 0.0456\n",
            "Epoch 23/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0101 - val_loss: 0.0182\n",
            "Epoch 24/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0015 - val_loss: 0.0323\n",
            "Epoch 25/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0033 - val_loss: 0.0272\n",
            "Epoch 26/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0021 - val_loss: 0.0204\n",
            "Epoch 27/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0017 - val_loss: 0.0155\n",
            "Epoch 28/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0024 - val_loss: 0.0119\n",
            "Epoch 29/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0043 - val_loss: 0.0421\n",
            "Epoch 30/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0022 - val_loss: 0.0111\n",
            "Epoch 31/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0014 - val_loss: 0.0069\n",
            "Epoch 32/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0011 - val_loss: 0.0037\n",
            "Epoch 33/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 8.0184e-04 - val_loss: 0.0032\n",
            "Epoch 34/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 7.2655e-04 - val_loss: 0.0037\n",
            "Epoch 35/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 6.1133e-04 - val_loss: 0.0032\n",
            "Epoch 36/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 7.0734e-04 - val_loss: 0.0038\n",
            "Epoch 37/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 6.2283e-04 - val_loss: 0.0037\n",
            "Epoch 38/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 7.6743e-04 - val_loss: 0.0041\n",
            "Epoch 39/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 9.1090e-04 - val_loss: 0.0062\n",
            "Epoch 40/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0012 - val_loss: 0.0052\n",
            "Epoch 41/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0014 - val_loss: 0.0123\n",
            "Epoch 42/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0014 - val_loss: 0.0063\n",
            "Epoch 43/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 9.3193e-04 - val_loss: 0.0078\n",
            "Epoch 44/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 0.0010 - val_loss: 0.0055\n",
            "Epoch 45/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 6.7466e-04 - val_loss: 0.0061\n",
            "Epoch 46/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 8.3506e-04 - val_loss: 0.0051\n",
            "Epoch 47/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 6.0600e-04 - val_loss: 0.0060\n",
            "Epoch 48/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 7.5372e-04 - val_loss: 0.0052\n",
            "Epoch 49/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 6.1175e-04 - val_loss: 0.0071\n",
            "Epoch 50/50\n",
            "2650/2650 [==============================] - 6s 2ms/sample - loss: 8.1458e-04 - val_loss: 0.0056\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L9JumIeP40xv",
        "colab_type": "code",
        "outputId": "ac7cfeaf-7540-4734-a82c-394383378b95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "156/156 [==============================] - 0s 1ms/sample - loss: 0.0016\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0015700155860171295"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "2_9ibR1e5DnQ",
        "colab_type": "code",
        "outputId": "793cdf41-fc6b-4b4c-9959-82bcd6593625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2UAAAIGCAYAAADUYScTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Xf9STH2D4_hP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ]
    },
    {
      "metadata": {
        "id": "Lui1XTi0xVsF",
        "colab_type": "code",
        "outputId": "8aed2935-7395-474c-f5ba-c3dd0d8a423d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        }
      },
      "cell_type": "code",
      "source": [
        "y_hat = model.predict(X_test)\n",
        "\n",
        "y_test_inverse = scaler.inverse_transform(y_test)\n",
        "y_hat_inverse = scaler.inverse_transform(y_hat)\n",
        " \n",
        "plt.plot(y_test_inverse, label=\"Actual Price\", color='green')\n",
        "plt.plot(y_hat_inverse, label=\"Predicted Price\", color='red')\n",
        " \n",
        "plt.title('Bitcoin price prediction')\n",
        "plt.xlabel('Time [days]')\n",
        "plt.ylabel('Price')\n",
        "plt.legend(loc='best')\n",
        " \n",
        "plt.show();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2sAAAIGCAYAAADKqBegAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ak-CiQAVz4Fw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}